Source code for pyarrow.hdfs

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import os
import posixpath

from pyarrow.util import implements
from pyarrow.filesystem import FileSystem
import pyarrow.lib as lib


[docs]class HadoopFileSystem(lib.HadoopFileSystem, FileSystem): """ FileSystem interface for HDFS cluster. See pyarrow.hdfs.connect for full connection details """ def __init__(self, host="default", port=0, user=None, kerb_ticket=None, driver='libhdfs'): if driver == 'libhdfs': _maybe_set_hadoop_classpath() self._connect(host, port, user, kerb_ticket, driver) @implements(FileSystem.isdir) def isdir(self, path): return super(HadoopFileSystem, self).isdir(path) @implements(FileSystem.isfile) def isfile(self, path): return super(HadoopFileSystem, self).isfile(path)
[docs] @implements(FileSystem.delete) def delete(self, path, recursive=False): return super(HadoopFileSystem, self).delete(path, recursive)
[docs] def mkdir(self, path, **kwargs): """ Create directory in HDFS Parameters ---------- path : string Directory path to create, including any parent directories Notes ----- libhdfs does not support create_parents=False, so we ignore this here """ return super(HadoopFileSystem, self).mkdir(path)
[docs] @implements(FileSystem.rename) def rename(self, path, new_path): return super(HadoopFileSystem, self).rename(path, new_path)
[docs] @implements(FileSystem.exists) def exists(self, path): return super(HadoopFileSystem, self).exists(path)
[docs] def ls(self, path, detail=False): """ Retrieve directory contents and metadata, if requested. Parameters ---------- path : HDFS path detail : boolean, default False If False, only return list of paths Returns ------- result : list of dicts (detail=True) or strings (detail=False) """ return super(HadoopFileSystem, self).ls(path, detail)
def walk(self, top_path): """ Directory tree generator for HDFS, like os.walk Parameters ---------- top_path : string Root directory for tree traversal Returns ------- Generator yielding 3-tuple (dirpath, dirnames, filename) """ contents = self.ls(top_path, detail=True) directories, files = _libhdfs_walk_files_dirs(top_path, contents) yield top_path, directories, files for dirname in directories: for tup in self.walk(self._path_join(top_path, dirname)): yield tup
def _maybe_set_hadoop_classpath(): import subprocess if 'hadoop' in os.environ.get('CLASSPATH', ''): return if 'HADOOP_HOME' in os.environ: hadoop_bin = '{0}/bin/hadoop'.format(os.environ['HADOOP_HOME']) else: hadoop_bin = 'hadoop' classpath = subprocess.check_output([hadoop_bin, 'classpath', '--glob']) os.environ['CLASSPATH'] = classpath.decode('utf-8') def _libhdfs_walk_files_dirs(top_path, contents): files = [] directories = [] for c in contents: scrubbed_name = posixpath.split(c['name'])[1] if c['kind'] == 'file': files.append(scrubbed_name) else: directories.append(scrubbed_name) return directories, files
[docs]def connect(host="default", port=0, user=None, kerb_ticket=None, driver='libhdfs'): """ Connect to an HDFS cluster. All parameters are optional and should only be set if the defaults need to be overridden. Authentication should be automatic if the HDFS cluster uses Kerberos. However, if a username is specified, then the ticket cache will likely be required. Parameters ---------- host : NameNode. Set to "default" for fs.defaultFS from core-site.xml. port : NameNode's port. Set to 0 for default or logical (HA) nodes. user : Username when connecting to HDFS; None implies login user. kerb_ticket : Path to Kerberos ticket cache. driver : {'libhdfs', 'libhdfs3'}, default 'libhdfs' Connect using libhdfs (JNI-based) or libhdfs3 (3rd-party C++ library from Apache HAWQ (incubating) ) Notes ----- The first time you call this method, it will take longer than usual due to JNI spin-up time. Returns ------- filesystem : HadoopFileSystem """ fs = HadoopFileSystem(host=host, port=port, user=user, kerb_ticket=kerb_ticket, driver=driver) return fs